Based on the mat1_1_1 and mat1_1_2 loci from Leptographium procerum, I did depth assays to see presence/absence of genes.
After searching for the homolog proteins, I found that the regions of CMW154_Contig94:9951-11159 that includes the two proteins LWAP_08037-RA and LWAP_08038-RA that are the best BLAST hits to the MAT1_1_1 gene and MAT1_2_1
Lets see how they look.
mat1 <- readRDS("mat1_depth.Rds")
mat1.pop2 <- readRDS("mat1_others.Rds")
colnames(mat1) <- gsub(colnames(mat1), pattern = "_dupmrk.bam", replacement = "")
mat1 <- merge(mat1, mat1.pop2, by = c("Chrom","Pos"))
library(reshape2)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.1 ✓ purrr 0.3.4
## ✓ tibble 3.0.1 ✓ dplyr 1.0.0
## ✓ tidyr 1.1.0 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(viridis)
## Loading required package: viridisLite
mat1.melt <- melt(mat1, id=c("Chrom","Pos"))
We have 3 genes: MAT1-1-1, MAT1-2-1 and the SLA gene.
Lets see how this looks like:
sla <- mat1[mat1$Pos > 14590 & mat1$Pos < 17898, -c(1,2)]
mat1_1_1 <- mat1[mat1$Pos > 11159 & mat1$Pos < 13083, -c(1,2)]
mat1_2_1 <- mat1[mat1$Pos > 9951 & mat1$Pos < 10800, -c(1,2)]
sla.avg <- apply(sla, 2, function (y){ sum(y > 0) })/nrow(sla) * 100
sla.avg <- tibble("sample"=names(sla.avg),sla.avg)
mat1_1_1.avg <- apply(mat1_1_1, 2, function (y){ sum(y > 0) })/nrow(mat1_1_1) * 100
mat1_1_1.avg <- tibble("sample"=names(mat1_1_1.avg),mat1_1_1.avg)
mat1_2_1.avg <- apply(mat1_2_1, 2, function (y){ sum(y > 0) })/nrow(mat1_2_1) * 100
mat1_2_1.avg <- tibble("sample"=names(mat1_2_1.avg),mat1_2_1.avg)
all.cov <- merge(sla.avg,mat1_1_1.avg, by = "sample" ) %>% merge(.,mat1_2_1.avg, by = "sample" )
all.cov.m <- as.matrix(all.cov[,-1])
rownames(all.cov.m) <- all.cov[,1]
vcfR::heatmap.bp(all.cov.m)
So, in that case:
mat1 <- all.cov[all.cov$sla.avg > 99 & all.cov$mat1_1_1.avg > 99 & all.cov$mat1_2_1.avg > 99,]
mat1$mat <- "MAT_1"
mat2 <- all.cov[!(all.cov$sla.avg > 99 & all.cov$mat1_1_1.avg > 99 & all.cov$mat1_2_1.avg > 99),]
mat2$mat <- "MAT_2"
mat.ann <- rbind(mat1,mat2)
mat.ann$mat <- factor(mat.ann$mat)
mat.ann.raw <- mat.ann
library(kableExtra)
##
## Attaching package: 'kableExtra'
## The following object is masked from 'package:dplyr':
##
## group_rows
library(formattable)
mat.ann <- mat.ann %>% mutate(
SLA = ifelse(sla.avg > 90,
cell_spec(sla.avg, color = "green", bold = T),
cell_spec(sla.avg, color = "red", italic = T)),
MAT1_1_1= ifelse(mat1_1_1.avg > 90,
cell_spec(mat1_1_1.avg, color = "green", bold = T),
cell_spec(mat1_1_1.avg, color = "red", italic = T)),
MAT1_2_1 = ifelse(mat1_2_1.avg > 90,
cell_spec(mat1_2_1.avg, color = "green", bold = T),
cell_spec(mat1_2_1.avg, color = "red", italic = T)),
mat = color_tile("blue", "orange")(mat)
)
mat.ann [,-c(2,3,4)] %>%
kable(escape = F) %>%
kable_styling("hover", full_width = F) %>%
column_spec(5, width = "3cm")
| sample | mat | SLA | MAT1_1_1 | MAT1_2_1 |
|---|---|---|---|---|
| CABL1 | MAT_1 | 99.969761112791 | 99.8439937597504 | 100 |
| CABL2 | MAT_1 | 100 | 99.9479979199168 | 100 |
| CABL3 | MAT_1 | 100 | 99.9479979199168 | 100 |
| CADC02 | MAT_1 | 100 | 99.8439937597504 | 100 |
| CADC03 | MAT_1 | 99.969761112791 | 99.5839833593344 | 100 |
| CADC06 | MAT_1 | 99.9395222255821 | 99.6359854394176 | 100 |
| MP01 | MAT_1 | 100 | 99.9479979199168 | 100 |
| MP03 | MAT_1 | 100 | 99.8439937597504 | 100 |
| MP04 | MAT_1 | 100 | 100 | 100 |
| MP06 | MAT_1 | 100 | 100 | 100 |
| MP08 | MAT_1 | 100 | 99.9479979199168 | 100 |
| MP09 | MAT_1 | 100 | 99.9479979199168 | 100 |
| MP10 | MAT_1 | 100 | 99.8439937597504 | 100 |
| STF1015-01 | MAT_1 | 100 | 100 | 100 |
| STF1015-09 | MAT_1 | 100 | 100 | 100 |
| STF1015-13 | MAT_1 | 100 | 100 | 100 |
| STF1015-14 | MAT_1 | 99.6068944662836 | 100 | 100 |
| STF1015-15 | MAT_1 | 100 | 100 | 100 |
| STF1032-07 | MAT_1 | 100 | 100 | 100 |
| STF1032-12 | MAT_1 | 100 | 100 | 100 |
| STF1032-13 | MAT_1 | 100 | 100 | 100 |
| STFO706-01 | MAT_1 | 100 | 100 | 100 |
| STFO706-04 | MAT_1 | 100 | 100 | 100 |
| STFO706-05 | MAT_1 | 100 | 100 | 100 |
| STFO7A2-02 | MAT_1 | 100 | 100 | 100 |
| STFO7A2-03 | MAT_1 | 100 | 100 | 100 |
| STFO7A2-04 | MAT_1 | 100 | 100 | 100 |
| STFO7A2-05 | MAT_1 | 100 | 100 | 100 |
| STFO7A2-06 | MAT_1 | 99.6976111279105 | 100 | 100 |
| STFO7A2-07 | MAT_1 | 100 | 100 | 100 |
| STFO7A2-11 | MAT_1 | 100 | 100 | 100 |
| STFO9A5-01 | MAT_1 | 100 | 100 | 100 |
| STFO9A5-02 | MAT_1 | 100 | 100 | 100 |
| STFO9A5-04 | MAT_1 | 100 | 99.9479979199168 | 100 |
| STFO9A5-07 | MAT_1 | 100 | 99.9479979199168 | 100 |
| STFO9A5-08 | MAT_1 | 100 | 100 | 100 |
| STFO9A5-10 | MAT_1 | 100 | 99.6359854394176 | 100 |
| WRTF5180-08 | MAT_1 | 100 | 100 | 100 |
| WRTF5180-09 | MAT_1 | 100 | 100 | 100 |
| WRTF5180-10 | MAT_1 | 100 | 100 | 100 |
| WRTF5180-11 | MAT_1 | 100 | 99.9479979199168 | 100 |
| WRTF5180-12 | MAT_1 | 100 | 100 | 100 |
| WRTF5360-01 | MAT_1 | 100 | 100 | 100 |
| WRTF5360-02 | MAT_1 | 100 | 100 | 100 |
| WRTF5360-03 | MAT_1 | 100 | 100 | 100 |
| WRTF5360-05 | MAT_1 | 100 | 100 | 100 |
| WRTF5360-06 | MAT_1 | 100 | 100 | 100 |
| WRTF5360-10 | MAT_1 | 100 | 100 | 100 |
| WRTF5360-11 | MAT_1 | 100 | 100 | 100 |
| WRTF5360-12 | MAT_1 | 100 | 100 | 100 |
| WRTF5480-01 | MAT_1 | 99.969761112791 | 100 | 100 |
| WRTF5480-03 | MAT_1 | 100 | 100 | 100 |
| WRTF5480-04 | MAT_1 | 100 | 100 | 100 |
| WRTF5480-05 | MAT_1 | 99.969761112791 | 100 | 100 |
| WRTF5480-07 | MAT_1 | 99.969761112791 | 100 | 100 |
| WRTF5480-12 | MAT_1 | 100 | 99.9479979199168 | 100 |
| WRTF5481B-02 | MAT_1 | 100 | 100 | 100 |
| WRTF5481B-07 | MAT_1 | 100 | 100 | 100 |
| WRTF5481B-13 | MAT_1 | 100 | 100 | 100 |
| WRTF5481B-15 | MAT_1 | 100 | 100 | 100 |
| WRTF8800-02 | MAT_1 | 100 | 100 | 100 |
| WRTF8800-03 | MAT_1 | 100 | 100 | 100 |
| WRTF8800-05 | MAT_1 | 100 | 100 | 100 |
| WRTF8800-06 | MAT_1 | 100 | 100 | 100 |
| WRTF8800-09 | MAT_1 | 100 | 100 | 100 |
| WRTF8800-12 | MAT_1 | 99.969761112791 | 100 | 100 |
| WSTF230-02 | MAT_1 | 100 | 100 | 100 |
| WSTF230-03 | MAT_1 | 100 | 100 | 100 |
| WSTF230-04 | MAT_1 | 100 | 100 | 100 |
| WSTF230-05 | MAT_1 | 100 | 100 | 100 |
| WSTF230-06 | MAT_1 | 100 | 100 | 100 |
| WSTF230-11 | MAT_1 | 100 | 100 | 100 |
| WSTF700-01 | MAT_1 | 100 | 100 | 99.8820754716981 |
| WSTF700-03 | MAT_1 | 100 | 100 | 100 |
| WSTF700-04 | MAT_1 | 100 | 100 | 100 |
| WSTF700-06 | MAT_1 | 100 | 100 | 100 |
| WSTF700-07 | MAT_1 | 100 | 100 | 99.8820754716981 |
| WSTF700-08 | MAT_1 | 100 | 99.8959958398336 | 100 |
| WSTF700-12 | MAT_1 | 100 | 100 | 100 |
| WSTF700-13 | MAT_1 | 100 | 99.9479979199168 | 100 |
| WSTF7280-01 | MAT_1 | 100 | 100 | 100 |
| WSTF7280-02 | MAT_1 | 100 | 100 | 100 |
| WSTF7280-03 | MAT_1 | 100 | 100 | 100 |
| WSTF7280-04 | MAT_1 | 100 | 100 | 100 |
| WSTF7280-05 | MAT_1 | 100 | 100 | 100 |
| WSTF7280-06 | MAT_1 | 100 | 100 | 100 |
| WSTF7280-07 | MAT_1 | 100 | 100 | 100 |
| WSTF7280-08 | MAT_1 | 100 | 100 | 100 |
| WSTF7280-09 | MAT_1 | 100 | 100 | 100 |
| WSTF7280-10 | MAT_1 | 100 | 99.9479979199168 | 100 |
| WSTF7280-11 | MAT_1 | 100 | 100 | 100 |
| WSTF7280-12 | MAT_1 | 100 | 100 | 100 |
| WSTF7280-13 | MAT_1 | 100 | 100 | 100 |
| WSTF730-01 | MAT_1 | 100 | 100 | 100 |
| WSTF730-02 | MAT_1 | 100 | 100 | 100 |
| WSTF730-03 | MAT_1 | 100 | 100 | 100 |
| WSTF730-04 | MAT_1 | 100 | 100 | 100 |
| WSTF730-05 | MAT_1 | 100 | 99.8439937597504 | 100 |
| WSTF730-06 | MAT_1 | 100 | 99.7919916796672 | 100 |
| WSTF730-07 | MAT_1 | 100 | 100 | 100 |
| WSTF730-08 | MAT_1 | 100 | 100 | 100 |
| WSTF730-09 | MAT_1 | 100 | 100 | 100 |
| WSTF730-10 | MAT_1 | 100 | 100 | 100 |
| WSTF730-11 | MAT_1 | 100 | 100 | 100 |
| WSTF770-01 | MAT_1 | 100 | 100 | 100 |
| WSTF770-02 | MAT_1 | 100 | 99.8959958398336 | 100 |
| WSTF770-03 | MAT_1 | 100 | 100 | 100 |
| WSTF770-05 | MAT_1 | 100 | 99.9479979199168 | 100 |
| WSTF770-08 | MAT_1 | 100 | 100 | 100 |
| WSTF770-09 | MAT_1 | 100 | 100 | 100 |
| WSTF770-10 | MAT_1 | 100 | 99.9479979199168 | 100 |
| MP02 | MAT_2 | 100 | 85.2314092563703 | 0 |
| MP05 | MAT_2 | 100 | 85.3354134165367 | 0 |
| MP11 | MAT_2 | 100 | 87.1034841393656 | 0 |
| STF1015-16 | MAT_2 | 100 | 85.2834113364535 | 0 |
| STF1015-19 | MAT_2 | 100 | 85.2834113364535 | 0 |
| STF1015-2X | MAT_2 | 100 | 85.2834113364535 | 0 |
| STF1015-2Y | MAT_2 | 100 | 85.3354134165367 | 0 |
| STF1032-01 | MAT_2 | 100 | 85.2834113364535 | 24.0566037735849 |
| STF1032-04 | MAT_2 | 100 | 85.3874154966199 | 0 |
| STF1032-05 | MAT_2 | 100 | 87.207488299532 | 5.54245283018868 |
| STF1032-08 | MAT_2 | 100 | 87.7795111804472 | 0 |
| STF1032-09 | MAT_2 | 100 | 85.2834113364535 | 0 |
| STF1032-10 | MAT_2 | 100 | 85.3874154966199 | 0 |
| STF1032-11 | MAT_2 | 100 | 85.3354134165367 | 17.8066037735849 |
| STFO706-06 | MAT_2 | 100 | 85.2834113364535 | 0 |
| STFO706-08 | MAT_2 | 100 | 97.2438897555902 | 0 |
| STFO706-09 | MAT_2 | 100 | 85.3874154966199 | 0 |
| STFO706-11 | MAT_2 | 100 | 89.0275611024441 | 0 |
| STFO706-16 | MAT_2 | 100 | 85.2834113364535 | 0 |
| STFO706-17 | MAT_2 | 100 | 85.3874154966199 | 19.3396226415094 |
| STFO706-18 | MAT_2 | 100 | 85.3354134165367 | 0 |
| STFO7A2-01 | MAT_2 | 100 | 85.3874154966199 | 0 |
| STFO7A2-08 | MAT_2 | 100 | 85.3354134165367 | 0 |
| STFO7A2-09 | MAT_2 | 100 | 85.2834113364535 | 35.2594339622642 |
| STFO9A5-06 | MAT_2 | 100 | 85.2834113364535 | 0 |
| STFO9A5-09 | MAT_2 | 100 | 85.2834113364535 | 20.7547169811321 |
| WRTF5180-01 | MAT_2 | 100 | 85.2834113364535 | 0 |
| WRTF5180-02 | MAT_2 | 100 | 85.2834113364535 | 0 |
| WRTF5180-03 | MAT_2 | 100 | 85.2834113364535 | 0 |
| WRTF5180-04 | MAT_2 | 100 | 85.3354134165367 | 0 |
| WRTF5180-06 | MAT_2 | 100 | 90.3796151846074 | 9.31603773584906 |
| WRTF5180-07 | MAT_2 | 100 | 85.3354134165367 | 13.4433962264151 |
| WRTF5360-07 | MAT_2 | 100 | 85.3354134165367 | 0 |
| WRTF5360-08 | MAT_2 | 100 | 85.2834113364535 | 0 |
| WRTF5360-09 | MAT_2 | 100 | 85.2834113364535 | 0 |
| WRTF5481B-01 | MAT_2 | 100 | 85.2834113364535 | 0 |
| WRTF5481B-03 | MAT_2 | 100 | 85.2834113364535 | 0 |
| WRTF5481B-05 | MAT_2 | 100 | 90.275611024441 | 0 |
| WRTF5481B-09 | MAT_2 | 100 | 85.3874154966199 | 0 |
| WRTF5481B-10 | MAT_2 | 100 | 85.2834113364535 | 0 |
| WRTF5481B-11 | MAT_2 | 100 | 85.2834113364535 | 0 |
| WRTF5481B-12 | MAT_2 | 100 | 85.1274050962038 | 0 |
| WRTF5481B-14 | MAT_2 | 100 | 85.3354134165367 | 0 |
| WRTF8800-01 | MAT_2 | 100 | 85.2834113364535 | 0 |
| WRTF8800-08 | MAT_2 | 100 | 85.2834113364535 | 0 |
| WRTF8800-10 | MAT_2 | 100 | 85.2834113364535 | 0 |
| WRTF8800-11 | MAT_2 | 98.8811611732688 | 85.2834113364535 | 35.6132075471698 |
| WSTF230-09 | MAT_2 | 100 | 85.2834113364535 | 27.8301886792453 |
| WSTF230-13 | MAT_2 | 100 | 93.7077483099324 | 25.5896226415094 |
| WSTF700-10 | MAT_2 | 100 | 96.0478419136765 | 0 |
| WSTF770-04 | MAT_2 | 100 | 85.2834113364535 | 0 |
| WSTF770-06 | MAT_2 | 100 | 85.2314092563703 | 0 |
| WSTF770-11 | MAT_2 | 100 | 85.3354134165367 | 0 |
colnames(mat1.melt) <- c("Chrom","Pos","sample","Depth")
mat1.melt <- merge(mat1.melt, mat.ann.raw[,c(1,5)], by = "sample")
ggplot(mat1.melt[mat1.melt$mat %in% "MAT_1",], aes(x=Pos, y=Depth)) + geom_line() + facet_grid(sample~.) +
geom_segment(aes(y=100, yend=100, x=9951, xend=10800, color="MAT1_2_1"), arrow = arrow(length = unit(0.1, "inches")), size=3) +
geom_segment(aes(y=100, yend=100,x=11159, xend=13083, color="MAT1_1_1"), arrow = arrow(length = unit(0.1, "inches")), size=3) +
geom_segment(aes(y=100, yend=100, x=14590, xend=17898, color="SLA"), arrow = arrow(length = unit(0.1, "inches")), size=3) + theme_classic()
ggplot(mat1.melt[mat1.melt$mat %in% "MAT_2",], aes(x=Pos, y=Depth)) + geom_line() + facet_grid(sample ~ .) +
geom_segment(aes(y=100, yend=100, x=9951, xend=10800, color="MAT1_2_1"), arrow = arrow(length = unit(0.1, "inches")), size=3) +
geom_segment(aes(y=100, yend=100,x=11159, xend=13083, color="MAT1_1_1"), arrow = arrow(length = unit(0.1, "inches")), size=3) +
geom_segment(aes(y=100, yend=100, x=14590, xend=17898, color="SLA"), arrow = arrow(length = unit(0.1, "inches")), size=3) + theme_classic()
The idea is to test that the mating types are in a 1:1 proportion in the population in order to propose that sexual reproduction is occurring. Lets test that hypothesis:
library(poppr)
## Loading required package: adegenet
## Loading required package: ade4
## Registered S3 method overwritten by 'spdep':
## method from
## plot.mst ape
##
## /// adegenet 2.1.3 is loaded ////////////
##
## > overview: '?adegenet'
## > tutorials/doc/questions: 'adegenetWeb()'
## > bug reports/feature requests: adegenetIssues()
## Registered S3 method overwritten by 'pegas':
## method from
## print.amova ade4
## This is poppr version 2.8.5. To get started, type package?poppr
## OMP parallel support: available
# Setting up population vectors:
lepto.snp <- readRDS("LeptoPNW.snpclone")
# Removing unused samples
mat.ann.raw <- mat.ann.raw[mat.ann.raw$sample %in% indNames(lepto.snp), ]
pvals.mat <- lapply(levels(lepto.snp@strata$Site), function (x) {
samples.site <- rownames(lepto.snp@strata[lepto.snp@strata$Site %in% x,])
mat.sub <- mat.ann.raw[mat.ann.raw$sample %in% samples.site,]
mat.tab <- table(mat.sub$mat) %>% unname
max.prop <- max(mat.tab)/min(mat.tab) %>% round(digits = 2)
expect <- nrow(mat.sub)/2
chisq.val <- (table(mat.sub$mat)[1] - expect)^2/expect
chisq.val <- (5 - expect)^2/5
qchisq(chisq.val, df = 1)
chisq.val <- (table(mat.sub$mat)[1] - expect)^2/expect + (table(mat.sub$mat)[2] - expect)^2/expect
chisq.res <- chisq.test(table(mat.sub$mat), p = c(0.5,0.5))
data.frame("Site"=x, "MAT1"=table(mat.sub$mat)[1] %>% unname, "MAT2"=table(mat.sub$mat)[2] %>% unname, "Proportions"=paste0(round(max.prop,digits = 2),":",1), "Chisq"=chisq.res$p.value)
}) %>% bind_rows()
## Warning in qchisq(chisq.val, df = 1): NaNs produced
## Warning in qchisq(chisq.val, df = 1): NaNs produced
## Warning in qchisq(chisq.val, df = 1): NaNs produced
pvals.mat %>%
kable(escape = F) %>%
kable_styling("hover", full_width = F) %>%
column_spec(4, width = "3cm")
| Site | MAT1 | MAT2 | Proportions | Chisq |
|---|---|---|---|---|
| MP | 7 | 3 | 2.33:1 | 0.2059032 |
| STF | 24 | 23 | 1.04:1 | 0.8840280 |
| WRTF | 27 | 20 | 1.35:1 | 0.3072284 |
| WSTF | 44 | 6 | 7.33:1 | 0.0000001 |
pvals.mat <- lapply(levels(lepto.snp@strata$Stand), function (x) {
samples.site <- rownames(lepto.snp@strata[lepto.snp@strata$Stand %in% x,])
mat.sub <- mat.ann.raw[mat.ann.raw$sample %in% samples.site,]
mat.tab <- table(mat.sub$mat) %>% unname
max.prop <- max(mat.tab)/min(mat.tab) %>% round(digits = 2)
expect <- nrow(mat.sub)/2
chisq.val <- (table(mat.sub$mat)[1] - expect)^2/expect
chisq.val <- (5 - expect)^2/5
qchisq(chisq.val, df = 1)
chisq.val <- (table(mat.sub$mat)[1] - expect)^2/expect + (table(mat.sub$mat)[2] - expect)^2/expect
chisq.res <- chisq.test(table(mat.sub$mat), p = c(0.5,0.5))
chisq.res$p.value
data.frame("Stand"=x, "MAT1"=table(mat.sub$mat)[1] %>% unname, "MAT2"=table(mat.sub$mat)[2] %>% unname, "Proportions"=paste0(round(max.prop,digits = 2),":",1), "Chisq"=chisq.res$p.value)
}) %>% bind_rows()
## Warning in chisq.test(table(mat.sub$mat), p = c(0.5, 0.5)): Chi-squared
## approximation may be incorrect
## Warning in chisq.test(table(mat.sub$mat), p = c(0.5, 0.5)): Chi-squared
## approximation may be incorrect
## Warning in chisq.test(table(mat.sub$mat), p = c(0.5, 0.5)): Chi-squared
## approximation may be incorrect
## Warning in chisq.test(table(mat.sub$mat), p = c(0.5, 0.5)): Chi-squared
## approximation may be incorrect
## Warning in chisq.test(table(mat.sub$mat), p = c(0.5, 0.5)): Chi-squared
## approximation may be incorrect
## Warning in chisq.test(table(mat.sub$mat), p = c(0.5, 0.5)): Chi-squared
## approximation may be incorrect
pvals.mat %>%
kable(escape = F) %>%
kable_styling("hover", full_width = F) %>%
column_spec(4, width = "3cm")
| Stand | MAT1 | MAT2 | Proportions | Chisq |
|---|---|---|---|---|
| MP_MP | 7 | 3 | 2.33:1 | 0.2059032 |
| STF_1015 | 5 | 4 | 1.25:1 | 0.7388827 |
| STF_1032 | 3 | 7 | 2.33:1 | 0.2059032 |
| STF_O706 | 3 | 7 | 2.33:1 | 0.2059032 |
| STF_O7A2 | 7 | 3 | 2.33:1 | 0.2059032 |
| STF_O9A5 | 6 | 2 | 3:1 | 0.1572992 |
| WRTF_5180 | 5 | 6 | 1.2:1 | 0.7630246 |
| WRTF_5360 | 8 | 3 | 2.67:1 | 0.1316680 |
| WRTF_5480 | 6 | 0 | Inf:1 | 0.0143059 |
| WRTF_5481B | 3 | 8 | 2.67:1 | 0.1316680 |
| WRTF_8800 | 5 | 3 | 1.67:1 | 0.4795001 |
| WSTF_230 | 6 | 2 | 3:1 | 0.1572992 |
| WSTF_700 | 7 | 1 | 7:1 | 0.0338949 |
| WSTF_7280 | 13 | 0 | Inf:1 | 0.0003115 |
| WSTF_730 | 11 | 0 | Inf:1 | 0.0009111 |
| WSTF_770 | 7 | 3 | 2.33:1 | 0.2059032 |